IntentKV: Revolutionizing Long-Horizon LLM Performance

Long-horizon large language model (LLM) agents face a growing challenge. The bottleneck isn't in parameter compute anymore. It's the key-value (KV) cache that's holding back performance. That's where IntentKV comes in, a fresh approach to KV pruning that keeps the base LLM static, while cleverly managing memory.

The Cache Conundrum

Multi-turn LLM agents juggle complex tasks, resulting in short queries expanding into extensive trajectories of tool calls, search results, and reasoning. The KV memory and its read bandwidth balloon, turning the cache into a dominant bottleneck. Here's what the benchmarks actually show: maintaining performance under these conditions is tough.

IntentKV takes a novel approach. It keeps track of session-level queries, using memory attention rules to score live history tokens. Essentially, it adds a zero-initialized residual head that cross-attends over current-query K-vectors. What does this mean in practice? Less cache clutter and more efficient processing.

Performance Gains

Strip away the marketing and you get real numbers. At an 8,000 KV budget, IntentKV reduces mean peak request tokens by 23.9% on Qwen3-8B and 30.7% on Qwen2.5-14B. These aren't just marginal improvements. On the 100 longest BCP queries for Qwen2.5-14B, worst-case peak request tokens drop by a staggering 77.8% from 92.3k to 20.5k. Similarly, worst-case raw KV reads plummet by 92.6%, from 411 million to just 31 million.

Why It Matters

So why should anyone care about these numbers? The reality is, as LLMs evolve and tackle increasingly complex tasks, efficient KV management becomes important. The architecture matters more than the parameter count. IntentKV's ability to prune KV caches without compromising accuracy could prove important for the future of LLMs. It begs the question: can other models afford to ignore such advancements?

In a world where computational efficiency is king, IntentKV sets a new standard. The numbers tell a different story, one where long-horizon agents can thrive without being bogged down by their own memory constraints. The future of LLM efficiency might just hinge on innovations like this.